// This method creates a Metanework cognitive array
public static MetaNode createRSV2CognitiveArray(ref MetaNode limbs, ref MetaNode[] camlines)
{
MetaNode[] children;
// creating Leaves
// 1. Creating 1st level (0) STANN (1 MetaNode) for Right/Left Arm/Foot Sensors, including the pickups (Limbs)
MetaNode Limbs = MetaNode.createTreeLeaf(12, // number of inputs for each leaf
4, // range of input (0-3)
3, // 3 STANN Layers
20, // Number of Neurons in each layer (except the output layer)
4, // Number of binary outputs of the node
0.5, // STANN Threshold
0.6, // STANN Learning Rate (Quick)
null, // parents should be linked here
RandGen, // random number generator
0 // leaf index is 0
);
// 4. Creating 1st level STANN MetaNode for the Camera Input (8x8 frame abstraction)
MetaNode[] CamFrameLines = MetaNode.createTreeLeaves(8, // 8 metanodes, 1 for each line
8, // 8 line inputs
2, // thresholded input range (0 black, 1 -white)
3, // 3 STANN Layers
20, // 20 neurons per layer (except output)
3, // 3 STANN binary ouputs
0.5, // STANN threshold
0.5, // STANN Learning Rate (Quick)
null, // Parents are null for now...
RandGen, // Random Number Generator
2 // the starting index for these leaves is 2
);
// Creating TOP node (although if things go nice, we may create a level before the top node)
children = new MetaNode[9];
children[0] = Limbs;
int i;
for (i = 0; i < 8; i++)
{
children[1 + i] = CamFrameLines[i];
}
// ATTENTION! this node will using its output as input, therefore should include
// itself in the children array following creation
MetaNode Top = MetaNode.createHigherLevelNode(children, // children array
2, // 2 children
3, // 3 STANN Layers
30, // 30 neurons per layer
6, // 6 binary outputs (may have to reduce/increase it)
0.5, // STANN threshold
0.7, // fast learning rate
null, // NO Parents. we're at the top
0, // 0 number of parents
RandGen, // Random Number Generator
false, // node is NOT self trained
false, // Q-Learning disabled (for now...)
0.3, // Q -learning a param is 0.3
0.6, // Q - learning γ param is 0.6
1 // Level 2
);
// *** ADDING self into the children list
Top.addChild(Top);
// *************** Updating the parents entries of the MetaNodes in a bottom-up fashion *************
limbs = Limbs;
camlines = CamFrameLines;
return(Top);
}
// This method creates a Metanework cognitive array
public static MetaNode createLMCartCognitiveArray(ref MetaNode sonarnode, ref MetaNode[] camlinesnodes)
{
MetaNode[] children;
// creating Leaves
// 1. Creating 1st level (0) STANN (1 MetaNode) for Sonar Sensors
MetaNode TransducersNode = MetaNode.createTreeLeaf(8, // number of inputs
4, // range of input (0-3)
3, // 3 STANN Layers
20, // Number of Neurons in each layer (except the output layer)
5, // Number of binary outputs of the node
0.5, // STANN Threshold
0.6, // STANN Learning Rate (Quick)
null, // parents should be linked here
RandGen, // random number generator
0 // leaf index is 0
);
// 4. Creating 1st level STANN MetaNode for the Camera Input (8x8 frame abstraction)
MetaNode[] CamFrameLinesNodes = MetaNode.createTreeLeaves(8, // 8 metanodes, 1 for each line
8, // 8 line inputs
2, // thresholded input range (0 black, 1 -white)
3, // 3 STANN Layers
20, // 20 neurons per layer (except output)
3, // 3 STANN binary ouputs
0.5, // STANN threshold
0.5, // STANN Learning Rate (Quick)
null, // Parents are null for now...
RandGen, // Random Number Generator
2 // the starting index for these leaves is 2
);
// Creating TOP node (although if things go nice, we may create a level before the top node)
children = new MetaNode[10];
children[0] = TransducersNode;
int i;
for (i = 0; i < 8; i++)
{
children[1 + i] = CamFrameLinesNodes[i];
}
// ATTENTION! this node will using its output as input, therefore should include
// itself in the children array following creation
MetaNode Top = MetaNode.createHigherLevelNode(children, // children array
3, // 3 children
3, // 3 STANN Layers
25, // 25 neurons per layer
4, // 4 binary outputs
0.5, // STANN threshold
0.5, // fast learning rate
null, // NO Parents. we're at the top
0, // 0 number of parents
RandGen, // Random Number Generator
false, // node is NOT self trained
true, // Q-Learning enabled
0.3, // Q -learning a param is 0.3
0.6, // Q - learning γ param is 0.6
1 // Level 2
);
// *** ADDING self into the children list
Top.addChild(Top);
sonarnode = TransducersNode;
camlinesnodes = CamFrameLinesNodes;
return(Top);
}
